Recently, I've completed 30 Days of ML with Pytorch, where I explored all the major machine learning algorithms and basics of deep learning with concepts like activation, optimizer, loss functions etc. However, the idea behind starting 30 Days of ML with PyTorch is to learn the implementation of machine learning algorithm along with strengthening my grasp on PyTorch Library. So moving forward with this blog, I will share few important functions in PyTorch which remains part of every ML and DL algorithms. In neural networks, how we initialize our weights plays a great role in convergence of the model and a common but inefficient way to initialize weight is to declare weights as zeros, but other efficient way includes using uniform or normal or xaviers technique to initialize weights. It is applied for creating dropout functionality in neural network for regularization.

Non Maximum Suppression (NMS) is a technique used in numerous computer vision tasks. It is a class of algorithms to select one entity (e.g., bounding boxes) out of many overlapping entities. We can choose the selection criteria to arrive at the desired results. The criteria are most commonly some form of probability number and some form of overlap measure (e.g. This post will go over how it works and implement it in Python using the PyTorch framework.

In this blog, we will discuss a few of the most commonly used PyTorch functions used to perform different types of matrix operations. PyTorch is an open-source machine learning library. Tensors are classes in Pytorch used to store and perform different types of operations on multidimensional arrays. Pytorch is used for applications such as computer vision and natural language processing, primarily developed by Facebook's AI Research lab (FAIR). Although the Python interface is more polished and the primary focus of development, PyTorch also has a C interface.